Overview

Dataset statistics

Number of variables36
Number of observations2751
Missing cells28860
Missing cells (%)29.1%
Duplicate rows7
Duplicate rows (%)0.3%
Total size in memory2.7 MiB
Average record size in memory1020.2 B

Variable types

Categorical20
DateTime2
Numeric14

Dataset

DescriptionQuality-verified clinical data for JHB_Aurum_009
CreatorHEAT Research Programme
AuthorRP2 Clinical Data Team
URLhttps://github.com/Logic06183/RP2_dataoverview

Alerts

study_source has constant value "JHB_Aurum_009"Constant
latitude has constant value "-25.7479"Constant
longitude has constant value "28.2293"Constant
jhb_subregion has constant value "Eastern_JHB"Constant
city has constant value "Johannesburg"Constant
province has constant value "Gauteng"Constant
country has constant value "South Africa"Constant
Location of study follow-up has constant value "Aurum Institute - Multi-site Gauteng and Limpopo"Constant
study_site_location has constant value "Tembisa/East Rand (Aurum Institute)"Constant
Clinical Study ID has constant value "Tholimpilo_HIV_Linkage_Study"Constant
HIV_status has constant value "Positive"Constant
total_protein_extreme_flag has constant value "0.0"Constant
climate_p90_threshold has constant value "28.409"Constant
climate_p95_threshold has constant value "29.704"Constant
climate_p99_threshold has constant value "31.797"Constant
Dataset has 7 (0.3%) duplicate rowsDuplicates
climate_14d_mean_temp is highly overall correlated with climate_30d_mean_temp and 10 other fieldsHigh correlation
climate_30d_mean_temp is highly overall correlated with climate_14d_mean_temp and 10 other fieldsHigh correlation
climate_7d_max_temp is highly overall correlated with climate_14d_mean_temp and 6 other fieldsHigh correlation
climate_7d_mean_temp is highly overall correlated with climate_14d_mean_temp and 10 other fieldsHigh correlation
climate_daily_max_temp is highly overall correlated with climate_14d_mean_temp and 10 other fieldsHigh correlation
climate_daily_mean_temp is highly overall correlated with climate_14d_mean_temp and 11 other fieldsHigh correlation
climate_daily_min_temp is highly overall correlated with climate_14d_mean_temp and 10 other fieldsHigh correlation
climate_heat_day_p90 is highly overall correlated with climate_14d_mean_temp and 11 other fieldsHigh correlation
climate_heat_day_p95 is highly overall correlated with climate_14d_mean_temp and 11 other fieldsHigh correlation
climate_heat_stress_index is highly overall correlated with climate_14d_mean_temp and 10 other fieldsHigh correlation
climate_season is highly overall correlated with climate_14d_mean_temp and 13 other fieldsHigh correlation
climate_standardized_anomaly is highly overall correlated with climate_daily_mean_temp and 3 other fieldsHigh correlation
climate_temp_anomaly is highly overall correlated with climate_heat_day_p90 and 5 other fieldsHigh correlation
month is highly overall correlated with climate_heat_day_p90 and 3 other fieldsHigh correlation
year is highly overall correlated with climate_14d_mean_temp and 10 other fieldsHigh correlation
climate_heat_day_p90 is highly imbalanced (69.4%)Imbalance
climate_heat_day_p95 is highly imbalanced (69.4%)Imbalance
CD4 cell count (cells/µL) has 533 (19.4%) missing valuesMissing
HIV viral load (copies/mL) has 2461 (89.5%) missing valuesMissing
climate_daily_mean_temp has 1616 (58.7%) missing valuesMissing
climate_daily_max_temp has 1616 (58.7%) missing valuesMissing
climate_daily_min_temp has 1616 (58.7%) missing valuesMissing
climate_7d_mean_temp has 1616 (58.7%) missing valuesMissing
climate_7d_max_temp has 1616 (58.7%) missing valuesMissing
climate_14d_mean_temp has 1616 (58.7%) missing valuesMissing
climate_30d_mean_temp has 1616 (58.7%) missing valuesMissing
climate_temp_anomaly has 1616 (58.7%) missing valuesMissing
climate_standardized_anomaly has 1616 (58.7%) missing valuesMissing
climate_heat_day_p90 has 1616 (58.7%) missing valuesMissing
climate_heat_day_p95 has 1616 (58.7%) missing valuesMissing
climate_heat_stress_index has 1616 (58.7%) missing valuesMissing
climate_p90_threshold has 1616 (58.7%) missing valuesMissing
climate_p95_threshold has 1616 (58.7%) missing valuesMissing
climate_p99_threshold has 1616 (58.7%) missing valuesMissing
climate_season has 1616 (58.7%) missing valuesMissing
HIV viral load (copies/mL) has 246 (8.9%) zerosZeros

Reproduction

Analysis started2025-11-25 05:34:07.083354
Analysis finished2025-11-25 05:34:15.067774
Duration7.98 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

study_source
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size188.1 KiB
JHB_Aurum_009
2751 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters35763
Distinct characters10
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJHB_Aurum_009
2nd rowJHB_Aurum_009
3rd rowJHB_Aurum_009
4th rowJHB_Aurum_009
5th rowJHB_Aurum_009

Common Values

ValueCountFrequency (%)
JHB_Aurum_0092751
100.0%

Length

2025-11-25T07:34:15.088953image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:34:15.118894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
jhb_aurum_0092751
100.0%

Most occurring characters

ValueCountFrequency (%)
_5502
15.4%
u5502
15.4%
05502
15.4%
J2751
7.7%
H2751
7.7%
B2751
7.7%
A2751
7.7%
r2751
7.7%
m2751
7.7%
92751
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter11004
30.8%
Uppercase Letter11004
30.8%
Decimal Number8253
23.1%
Connector Punctuation5502
15.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
J2751
25.0%
H2751
25.0%
B2751
25.0%
A2751
25.0%
Lowercase Letter
ValueCountFrequency (%)
u5502
50.0%
r2751
25.0%
m2751
25.0%
Decimal Number
ValueCountFrequency (%)
05502
66.7%
92751
33.3%
Connector Punctuation
ValueCountFrequency (%)
_5502
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin22008
61.5%
Common13755
38.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
u5502
25.0%
J2751
12.5%
H2751
12.5%
B2751
12.5%
A2751
12.5%
r2751
12.5%
m2751
12.5%
Common
ValueCountFrequency (%)
_5502
40.0%
05502
40.0%
92751
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII35763
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_5502
15.4%
u5502
15.4%
05502
15.4%
J2751
7.7%
H2751
7.7%
B2751
7.7%
A2751
7.7%
r2751
7.7%
m2751
7.7%
92751
7.7%
Distinct447
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Memory size43.0 KiB
Minimum2013-03-14 00:00:00
Maximum2015-08-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-25T07:34:15.157268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:15.211924image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

year
Categorical

High correlation 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size169.3 KiB
2014.0
1677 
2013.0
1073 
2015.0
 
1

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters16506
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2014.0
2nd row2014.0
3rd row2014.0
4th row2014.0
5th row2013.0

Common Values

ValueCountFrequency (%)
2014.01677
61.0%
2013.01073
39.0%
2015.01
 
< 0.1%

Length

2025-11-25T07:34:15.262751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:34:15.297546image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2014.01677
61.0%
2013.01073
39.0%
2015.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
05502
33.3%
22751
16.7%
12751
16.7%
.2751
16.7%
41677
 
10.2%
31073
 
6.5%
51
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number13755
83.3%
Other Punctuation2751
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
05502
40.0%
22751
20.0%
12751
20.0%
41677
 
12.2%
31073
 
7.8%
51
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.2751
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common16506
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
05502
33.3%
22751
16.7%
12751
16.7%
.2751
16.7%
41677
 
10.2%
31073
 
6.5%
51
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII16506
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
05502
33.3%
22751
16.7%
12751
16.7%
.2751
16.7%
41677
 
10.2%
31073
 
6.5%
51
 
< 0.1%

month
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9465649
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.0 KiB
2025-11-25T07:34:15.330693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median7
Q310
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.9715711
Coefficient of variation (CV)0.42777561
Kurtosis-1.0043626
Mean6.9465649
Median Absolute Deviation (MAD)2
Skewness-0.30693242
Sum19110
Variance8.8302346
MonotonicityNot monotonic
2025-11-25T07:34:15.368141image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
10413
15.0%
7373
13.6%
9321
11.7%
8273
9.9%
2250
9.1%
5216
7.9%
11215
7.8%
4205
7.5%
6199
7.2%
3157
 
5.7%
Other values (2)129
 
4.7%
ValueCountFrequency (%)
162
 
2.3%
2250
9.1%
3157
 
5.7%
4205
7.5%
5216
7.9%
6199
7.2%
7373
13.6%
8273
9.9%
9321
11.7%
10413
15.0%
ValueCountFrequency (%)
1267
 
2.4%
11215
7.8%
10413
15.0%
9321
11.7%
8273
9.9%
7373
13.6%
6199
7.2%
5216
7.9%
4205
7.5%
3157
 
5.7%

latitude
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size174.6 KiB
-25.7479
2751 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters22008
Distinct characters7
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-25.7479
2nd row-25.7479
3rd row-25.7479
4th row-25.7479
5th row-25.7479

Common Values

ValueCountFrequency (%)
-25.74792751
100.0%

Length

2025-11-25T07:34:15.408751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:34:15.444013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
25.74792751
100.0%

Most occurring characters

ValueCountFrequency (%)
75502
25.0%
-2751
12.5%
22751
12.5%
52751
12.5%
.2751
12.5%
42751
12.5%
92751
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number16506
75.0%
Dash Punctuation2751
 
12.5%
Other Punctuation2751
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
75502
33.3%
22751
16.7%
52751
16.7%
42751
16.7%
92751
16.7%
Dash Punctuation
ValueCountFrequency (%)
-2751
100.0%
Other Punctuation
ValueCountFrequency (%)
.2751
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common22008
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
75502
25.0%
-2751
12.5%
22751
12.5%
52751
12.5%
.2751
12.5%
42751
12.5%
92751
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII22008
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
75502
25.0%
-2751
12.5%
22751
12.5%
52751
12.5%
.2751
12.5%
42751
12.5%
92751
12.5%

longitude
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size171.9 KiB
28.2293
2751 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters19257
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row28.2293
2nd row28.2293
3rd row28.2293
4th row28.2293
5th row28.2293

Common Values

ValueCountFrequency (%)
28.22932751
100.0%

Length

2025-11-25T07:34:15.479752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:34:15.512602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
28.22932751
100.0%

Most occurring characters

ValueCountFrequency (%)
28253
42.9%
82751
 
14.3%
.2751
 
14.3%
92751
 
14.3%
32751
 
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number16506
85.7%
Other Punctuation2751
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
28253
50.0%
82751
 
16.7%
92751
 
16.7%
32751
 
16.7%
Other Punctuation
ValueCountFrequency (%)
.2751
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common19257
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
28253
42.9%
82751
 
14.3%
.2751
 
14.3%
92751
 
14.3%
32751
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII19257
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
28253
42.9%
82751
 
14.3%
.2751
 
14.3%
92751
 
14.3%
32751
 
14.3%

jhb_subregion
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size182.7 KiB
Eastern_JHB
2751 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters30261
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEastern_JHB
2nd rowEastern_JHB
3rd rowEastern_JHB
4th rowEastern_JHB
5th rowEastern_JHB

Common Values

ValueCountFrequency (%)
Eastern_JHB2751
100.0%

Length

2025-11-25T07:34:15.546181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:34:15.575398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
eastern_jhb2751
100.0%

Most occurring characters

ValueCountFrequency (%)
E2751
9.1%
a2751
9.1%
s2751
9.1%
t2751
9.1%
e2751
9.1%
r2751
9.1%
n2751
9.1%
_2751
9.1%
J2751
9.1%
H2751
9.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter16506
54.5%
Uppercase Letter11004
36.4%
Connector Punctuation2751
 
9.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a2751
16.7%
s2751
16.7%
t2751
16.7%
e2751
16.7%
r2751
16.7%
n2751
16.7%
Uppercase Letter
ValueCountFrequency (%)
E2751
25.0%
J2751
25.0%
H2751
25.0%
B2751
25.0%
Connector Punctuation
ValueCountFrequency (%)
_2751
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin27510
90.9%
Common2751
 
9.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
E2751
10.0%
a2751
10.0%
s2751
10.0%
t2751
10.0%
e2751
10.0%
r2751
10.0%
n2751
10.0%
J2751
10.0%
H2751
10.0%
B2751
10.0%
Common
ValueCountFrequency (%)
_2751
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII30261
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E2751
9.1%
a2751
9.1%
s2751
9.1%
t2751
9.1%
e2751
9.1%
r2751
9.1%
n2751
9.1%
_2751
9.1%
J2751
9.1%
H2751
9.1%

city
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size185.4 KiB
Johannesburg
2751 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters33012
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJohannesburg
2nd rowJohannesburg
3rd rowJohannesburg
4th rowJohannesburg
5th rowJohannesburg

Common Values

ValueCountFrequency (%)
Johannesburg2751
100.0%

Length

2025-11-25T07:34:15.607710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:34:15.638967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
johannesburg2751
100.0%

Most occurring characters

ValueCountFrequency (%)
n5502
16.7%
J2751
8.3%
o2751
8.3%
h2751
8.3%
a2751
8.3%
e2751
8.3%
s2751
8.3%
b2751
8.3%
u2751
8.3%
r2751
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter30261
91.7%
Uppercase Letter2751
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n5502
18.2%
o2751
9.1%
h2751
9.1%
a2751
9.1%
e2751
9.1%
s2751
9.1%
b2751
9.1%
u2751
9.1%
r2751
9.1%
g2751
9.1%
Uppercase Letter
ValueCountFrequency (%)
J2751
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin33012
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n5502
16.7%
J2751
8.3%
o2751
8.3%
h2751
8.3%
a2751
8.3%
e2751
8.3%
s2751
8.3%
b2751
8.3%
u2751
8.3%
r2751
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII33012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n5502
16.7%
J2751
8.3%
o2751
8.3%
h2751
8.3%
a2751
8.3%
e2751
8.3%
s2751
8.3%
b2751
8.3%
u2751
8.3%
r2751
8.3%

province
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size171.9 KiB
Gauteng
2751 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters19257
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGauteng
2nd rowGauteng
3rd rowGauteng
4th rowGauteng
5th rowGauteng

Common Values

ValueCountFrequency (%)
Gauteng2751
100.0%

Length

2025-11-25T07:34:15.674099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:34:15.707918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
gauteng2751
100.0%

Most occurring characters

ValueCountFrequency (%)
G2751
14.3%
a2751
14.3%
u2751
14.3%
t2751
14.3%
e2751
14.3%
n2751
14.3%
g2751
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter16506
85.7%
Uppercase Letter2751
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a2751
16.7%
u2751
16.7%
t2751
16.7%
e2751
16.7%
n2751
16.7%
g2751
16.7%
Uppercase Letter
ValueCountFrequency (%)
G2751
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin19257
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G2751
14.3%
a2751
14.3%
u2751
14.3%
t2751
14.3%
e2751
14.3%
n2751
14.3%
g2751
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII19257
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G2751
14.3%
a2751
14.3%
u2751
14.3%
t2751
14.3%
e2751
14.3%
n2751
14.3%
g2751
14.3%

country
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size185.4 KiB
South Africa
2751 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters33012
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouth Africa
2nd rowSouth Africa
3rd rowSouth Africa
4th rowSouth Africa
5th rowSouth Africa

Common Values

ValueCountFrequency (%)
South Africa2751
100.0%

Length

2025-11-25T07:34:15.741203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:34:15.773196image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
south2751
50.0%
africa2751
50.0%

Most occurring characters

ValueCountFrequency (%)
S2751
8.3%
o2751
8.3%
u2751
8.3%
t2751
8.3%
h2751
8.3%
2751
8.3%
A2751
8.3%
f2751
8.3%
r2751
8.3%
i2751
8.3%
Other values (2)5502
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter24759
75.0%
Uppercase Letter5502
 
16.7%
Space Separator2751
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o2751
11.1%
u2751
11.1%
t2751
11.1%
h2751
11.1%
f2751
11.1%
r2751
11.1%
i2751
11.1%
c2751
11.1%
a2751
11.1%
Uppercase Letter
ValueCountFrequency (%)
S2751
50.0%
A2751
50.0%
Space Separator
ValueCountFrequency (%)
2751
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin30261
91.7%
Common2751
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
S2751
9.1%
o2751
9.1%
u2751
9.1%
t2751
9.1%
h2751
9.1%
A2751
9.1%
f2751
9.1%
r2751
9.1%
i2751
9.1%
c2751
9.1%
Common
ValueCountFrequency (%)
2751
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII33012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S2751
8.3%
o2751
8.3%
u2751
8.3%
t2751
8.3%
h2751
8.3%
2751
8.3%
A2751
8.3%
f2751
8.3%
r2751
8.3%
i2751
8.3%
Other values (2)5502
16.7%

Age (at enrolment)
Real number (ℝ)

Distinct59
Distinct (%)2.1%
Missing6
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean34.426958
Minimum15
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.0 KiB
2025-11-25T07:34:15.807527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile20
Q127
median33
Q340
95-th percentile54
Maximum76
Range61
Interquartile range (IQR)13

Descriptive statistics

Standard deviation10.178108
Coefficient of variation (CV)0.29564354
Kurtosis0.24473046
Mean34.426958
Median Absolute Deviation (MAD)7
Skewness0.70885633
Sum94502
Variance103.59388
MonotonicityNot monotonic
2025-11-25T07:34:15.852253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31125
 
4.5%
30117
 
4.3%
29116
 
4.2%
28113
 
4.1%
27108
 
3.9%
32106
 
3.9%
26104
 
3.8%
34102
 
3.7%
24101
 
3.7%
3397
 
3.5%
Other values (49)1656
60.2%
ValueCountFrequency (%)
154
 
0.1%
163
 
0.1%
1715
 
0.5%
1824
 
0.9%
1940
 
1.5%
2059
2.1%
2156
2.0%
2273
2.7%
2385
3.1%
24101
3.7%
ValueCountFrequency (%)
761
 
< 0.1%
741
 
< 0.1%
722
 
0.1%
711
 
< 0.1%
701
 
< 0.1%
692
 
0.1%
683
0.1%
671
 
< 0.1%
661
 
< 0.1%
655
0.2%

Sex
Categorical

Distinct2
Distinct (%)0.1%
Missing4
Missing (%)0.1%
Memory size165.9 KiB
Male
1708 
Female
1039 

Length

Max length6
Median length4
Mean length4.7564616
Min length4

Characters and Unicode

Total characters13066
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowMale
4th rowMale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male1708
62.1%
Female1039
37.8%
(Missing)4
 
0.1%

Length

2025-11-25T07:34:15.899492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:34:15.936380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
male1708
62.2%
female1039
37.8%

Most occurring characters

ValueCountFrequency (%)
e3786
29.0%
a2747
21.0%
l2747
21.0%
M1708
13.1%
F1039
 
8.0%
m1039
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter10319
79.0%
Uppercase Letter2747
 
21.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e3786
36.7%
a2747
26.6%
l2747
26.6%
m1039
 
10.1%
Uppercase Letter
ValueCountFrequency (%)
M1708
62.2%
F1039
37.8%

Most occurring scripts

ValueCountFrequency (%)
Latin13066
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e3786
29.0%
a2747
21.0%
l2747
21.0%
M1708
13.1%
F1039
 
8.0%
m1039
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII13066
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e3786
29.0%
a2747
21.0%
l2747
21.0%
M1708
13.1%
F1039
 
8.0%
m1039
 
8.0%

date
Date

Distinct447
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Memory size43.0 KiB
Minimum2013-03-14 00:00:00
Maximum2015-08-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-25T07:34:15.975786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:16.030408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Location of study follow-up
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size282.1 KiB
Aurum Institute - Multi-site Gauteng and Limpopo
2751 

Length

Max length48
Median length48
Mean length48
Min length48

Characters and Unicode

Total characters132048
Distinct characters21
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAurum Institute - Multi-site Gauteng and Limpopo
2nd rowAurum Institute - Multi-site Gauteng and Limpopo
3rd rowAurum Institute - Multi-site Gauteng and Limpopo
4th rowAurum Institute - Multi-site Gauteng and Limpopo
5th rowAurum Institute - Multi-site Gauteng and Limpopo

Common Values

ValueCountFrequency (%)
Aurum Institute - Multi-site Gauteng and Limpopo2751
100.0%

Length

2025-11-25T07:34:16.077311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:34:16.109031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
aurum2751
14.3%
institute2751
14.3%
2751
14.3%
multi-site2751
14.3%
gauteng2751
14.3%
and2751
14.3%
limpopo2751
14.3%

Most occurring characters

ValueCountFrequency (%)
16506
12.5%
t16506
12.5%
u13755
 
10.4%
i11004
 
8.3%
e8253
 
6.2%
n8253
 
6.2%
p5502
 
4.2%
a5502
 
4.2%
-5502
 
4.2%
o5502
 
4.2%
Other values (11)35763
27.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter96285
72.9%
Space Separator16506
 
12.5%
Uppercase Letter13755
 
10.4%
Dash Punctuation5502
 
4.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t16506
17.1%
u13755
14.3%
i11004
11.4%
e8253
8.6%
n8253
8.6%
p5502
 
5.7%
a5502
 
5.7%
o5502
 
5.7%
s5502
 
5.7%
m5502
 
5.7%
Other values (4)11004
11.4%
Uppercase Letter
ValueCountFrequency (%)
I2751
20.0%
M2751
20.0%
G2751
20.0%
L2751
20.0%
A2751
20.0%
Space Separator
ValueCountFrequency (%)
16506
100.0%
Dash Punctuation
ValueCountFrequency (%)
-5502
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin110040
83.3%
Common22008
 
16.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
t16506
15.0%
u13755
12.5%
i11004
10.0%
e8253
 
7.5%
n8253
 
7.5%
p5502
 
5.0%
a5502
 
5.0%
o5502
 
5.0%
s5502
 
5.0%
m5502
 
5.0%
Other values (9)24759
22.5%
Common
ValueCountFrequency (%)
16506
75.0%
-5502
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII132048
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
16506
12.5%
t16506
12.5%
u13755
 
10.4%
i11004
 
8.3%
e8253
 
6.2%
n8253
 
6.2%
p5502
 
4.2%
a5502
 
4.2%
-5502
 
4.2%
o5502
 
4.2%
Other values (11)35763
27.1%

study_site_location
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size247.2 KiB
Tembisa/East Rand (Aurum Institute)
2751 

Length

Max length35
Median length35
Mean length35
Min length35

Characters and Unicode

Total characters96285
Distinct characters20
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTembisa/East Rand (Aurum Institute)
2nd rowTembisa/East Rand (Aurum Institute)
3rd rowTembisa/East Rand (Aurum Institute)
4th rowTembisa/East Rand (Aurum Institute)
5th rowTembisa/East Rand (Aurum Institute)

Common Values

ValueCountFrequency (%)
Tembisa/East Rand (Aurum Institute)2751
100.0%

Length

2025-11-25T07:34:16.143308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:34:16.176450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
tembisa/east2751
25.0%
rand2751
25.0%
aurum2751
25.0%
institute2751
25.0%

Most occurring characters

ValueCountFrequency (%)
t11004
 
11.4%
8253
 
8.6%
s8253
 
8.6%
a8253
 
8.6%
u8253
 
8.6%
m5502
 
5.7%
i5502
 
5.7%
e5502
 
5.7%
n5502
 
5.7%
(2751
 
2.9%
Other values (10)27510
28.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter66024
68.6%
Uppercase Letter13755
 
14.3%
Space Separator8253
 
8.6%
Open Punctuation2751
 
2.9%
Other Punctuation2751
 
2.9%
Close Punctuation2751
 
2.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t11004
16.7%
s8253
12.5%
a8253
12.5%
u8253
12.5%
m5502
8.3%
i5502
8.3%
e5502
8.3%
n5502
8.3%
r2751
 
4.2%
d2751
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
I2751
20.0%
A2751
20.0%
T2751
20.0%
R2751
20.0%
E2751
20.0%
Space Separator
ValueCountFrequency (%)
8253
100.0%
Open Punctuation
ValueCountFrequency (%)
(2751
100.0%
Other Punctuation
ValueCountFrequency (%)
/2751
100.0%
Close Punctuation
ValueCountFrequency (%)
)2751
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin79779
82.9%
Common16506
 
17.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t11004
13.8%
s8253
10.3%
a8253
10.3%
u8253
10.3%
m5502
 
6.9%
i5502
 
6.9%
e5502
 
6.9%
n5502
 
6.9%
I2751
 
3.4%
r2751
 
3.4%
Other values (6)16506
20.7%
Common
ValueCountFrequency (%)
8253
50.0%
(2751
 
16.7%
/2751
 
16.7%
)2751
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII96285
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t11004
 
11.4%
8253
 
8.6%
s8253
 
8.6%
a8253
 
8.6%
u8253
 
8.6%
m5502
 
5.7%
i5502
 
5.7%
e5502
 
5.7%
n5502
 
5.7%
(2751
 
2.9%
Other values (10)27510
28.6%

CD4 cell count (cells/µL)
Real number (ℝ)

Missing 

Distinct854
Distinct (%)38.5%
Missing533
Missing (%)19.4%
Infinite0
Infinite (%)0.0%
Mean456.95807
Minimum3
Maximum2703
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.0 KiB
2025-11-25T07:34:16.211742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile108.85
Q1272
median416
Q3589
95-th percentile937
Maximum2703
Range2700
Interquartile range (IQR)317

Descriptive statistics

Standard deviation268.47946
Coefficient of variation (CV)0.58753632
Kurtosis7.1691831
Mean456.95807
Median Absolute Deviation (MAD)155
Skewness1.6497118
Sum1013533
Variance72081.223
MonotonicityNot monotonic
2025-11-25T07:34:16.259141image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3509
 
0.3%
3159
 
0.3%
5009
 
0.3%
4679
 
0.3%
4208
 
0.3%
3368
 
0.3%
4438
 
0.3%
3548
 
0.3%
4148
 
0.3%
5648
 
0.3%
Other values (844)2134
77.6%
(Missing)533
 
19.4%
ValueCountFrequency (%)
32
0.1%
61
< 0.1%
81
< 0.1%
101
< 0.1%
151
< 0.1%
161
< 0.1%
201
< 0.1%
211
< 0.1%
281
< 0.1%
291
< 0.1%
ValueCountFrequency (%)
27031
< 0.1%
26092
0.1%
19961
< 0.1%
17811
< 0.1%
17251
< 0.1%
15771
< 0.1%
15681
< 0.1%
15641
< 0.1%
15491
< 0.1%
15081
< 0.1%

HIV viral load (copies/mL)
Real number (ℝ)

Missing  Zeros 

Distinct45
Distinct (%)15.5%
Missing2461
Missing (%)89.5%
Infinite0
Infinite (%)0.0%
Mean20363.586
Minimum0
Maximum2670000
Zeros246
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size43.0 KiB
2025-11-25T07:34:16.304589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile7860.2
Maximum2670000
Range2670000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation196029.65
Coefficient of variation (CV)9.6264796
Kurtosis145.0072
Mean20363.586
Median Absolute Deviation (MAD)0
Skewness11.783887
Sum5905440
Variance3.8427622 × 1010
MonotonicityNot monotonic
2025-11-25T07:34:16.352619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0246
 
8.9%
85551
 
< 0.1%
3781
 
< 0.1%
24351
 
< 0.1%
64421
 
< 0.1%
137951
 
< 0.1%
2001
 
< 0.1%
311
 
< 0.1%
18981051
 
< 0.1%
1321
 
< 0.1%
Other values (35)35
 
1.3%
(Missing)2461
89.5%
ValueCountFrequency (%)
0246
8.9%
101
 
< 0.1%
311
 
< 0.1%
511
 
< 0.1%
741
 
< 0.1%
821
 
< 0.1%
871
 
< 0.1%
1321
 
< 0.1%
1431
 
< 0.1%
1741
 
< 0.1%
ValueCountFrequency (%)
26700001
< 0.1%
18981051
< 0.1%
6504421
< 0.1%
1643511
< 0.1%
1492471
< 0.1%
1250541
< 0.1%
440111
< 0.1%
385001
< 0.1%
348681
< 0.1%
222761
< 0.1%

Clinical Study ID
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size228.4 KiB
Tholimpilo_HIV_Linkage_Study
2751 

Length

Max length28
Median length28
Mean length28
Min length28

Characters and Unicode

Total characters77028
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTholimpilo_HIV_Linkage_Study
2nd rowTholimpilo_HIV_Linkage_Study
3rd rowTholimpilo_HIV_Linkage_Study
4th rowTholimpilo_HIV_Linkage_Study
5th rowTholimpilo_HIV_Linkage_Study

Common Values

ValueCountFrequency (%)
Tholimpilo_HIV_Linkage_Study2751
100.0%

Length

2025-11-25T07:34:16.396060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:34:16.427071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
tholimpilo_hiv_linkage_study2751
100.0%

Most occurring characters

ValueCountFrequency (%)
i8253
 
10.7%
_8253
 
10.7%
o5502
 
7.1%
l5502
 
7.1%
T2751
 
3.6%
k2751
 
3.6%
d2751
 
3.6%
u2751
 
3.6%
t2751
 
3.6%
S2751
 
3.6%
Other values (12)33012
42.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter52269
67.9%
Uppercase Letter16506
 
21.4%
Connector Punctuation8253
 
10.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i8253
15.8%
o5502
 
10.5%
l5502
 
10.5%
k2751
 
5.3%
d2751
 
5.3%
u2751
 
5.3%
t2751
 
5.3%
e2751
 
5.3%
g2751
 
5.3%
a2751
 
5.3%
Other values (5)13755
26.3%
Uppercase Letter
ValueCountFrequency (%)
T2751
16.7%
S2751
16.7%
L2751
16.7%
V2751
16.7%
I2751
16.7%
H2751
16.7%
Connector Punctuation
ValueCountFrequency (%)
_8253
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin68775
89.3%
Common8253
 
10.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
i8253
 
12.0%
o5502
 
8.0%
l5502
 
8.0%
T2751
 
4.0%
k2751
 
4.0%
d2751
 
4.0%
u2751
 
4.0%
t2751
 
4.0%
S2751
 
4.0%
e2751
 
4.0%
Other values (11)30261
44.0%
Common
ValueCountFrequency (%)
_8253
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII77028
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i8253
 
10.7%
_8253
 
10.7%
o5502
 
7.1%
l5502
 
7.1%
T2751
 
3.6%
k2751
 
3.6%
d2751
 
3.6%
u2751
 
3.6%
t2751
 
3.6%
S2751
 
3.6%
Other values (12)33012
42.9%

HIV_status
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size174.6 KiB
Positive
2751 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters22008
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPositive
2nd rowPositive
3rd rowPositive
4th rowPositive
5th rowPositive

Common Values

ValueCountFrequency (%)
Positive2751
100.0%

Length

2025-11-25T07:34:16.466317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:34:16.499692image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
positive2751
100.0%

Most occurring characters

ValueCountFrequency (%)
i5502
25.0%
P2751
12.5%
o2751
12.5%
s2751
12.5%
t2751
12.5%
v2751
12.5%
e2751
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter19257
87.5%
Uppercase Letter2751
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i5502
28.6%
o2751
14.3%
s2751
14.3%
t2751
14.3%
v2751
14.3%
e2751
14.3%
Uppercase Letter
ValueCountFrequency (%)
P2751
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin22008
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i5502
25.0%
P2751
12.5%
o2751
12.5%
s2751
12.5%
t2751
12.5%
v2751
12.5%
e2751
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII22008
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i5502
25.0%
P2751
12.5%
o2751
12.5%
s2751
12.5%
t2751
12.5%
v2751
12.5%
e2751
12.5%

total_protein_extreme_flag
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size161.2 KiB
0.0
2751 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters8253
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.02751
100.0%

Length

2025-11-25T07:34:16.532507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:34:16.564162image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.02751
100.0%

Most occurring characters

ValueCountFrequency (%)
05502
66.7%
.2751
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5502
66.7%
Other Punctuation2751
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
05502
100.0%
Other Punctuation
ValueCountFrequency (%)
.2751
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common8253
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
05502
66.7%
.2751
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII8253
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
05502
66.7%
.2751
33.3%

climate_daily_mean_temp
Real number (ℝ)

High correlation  Missing 

Distinct11
Distinct (%)1.0%
Missing1616
Missing (%)58.7%
Infinite0
Infinite (%)0.0%
Mean15.451807
Minimum9.356
Maximum23.589
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.0 KiB
2025-11-25T07:34:16.590250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum9.356
5-th percentile9.356
Q113.213
median14.195
Q319.293
95-th percentile23.589
Maximum23.589
Range14.233
Interquartile range (IQR)6.08

Descriptive statistics

Standard deviation3.5385321
Coefficient of variation (CV)0.22900442
Kurtosis-0.30036519
Mean15.451807
Median Absolute Deviation (MAD)0.982
Skewness0.47348153
Sum17537.801
Variance12.521209
MonotonicityNot monotonic
2025-11-25T07:34:16.624860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
19.293214
 
7.8%
13.213208
 
7.6%
14.195187
 
6.8%
13.868144
 
5.2%
9.35698
 
3.6%
18.20367
 
2.4%
23.58962
 
2.3%
13.65653
 
1.9%
13.31641
 
1.5%
17.79939
 
1.4%
(Missing)1616
58.7%
ValueCountFrequency (%)
9.35698
3.6%
13.213208
7.6%
13.31641
 
1.5%
13.65653
 
1.9%
13.868144
5.2%
14.195187
6.8%
17.79939
 
1.4%
18.20367
 
2.4%
19.293214
7.8%
20.29322
 
0.8%
ValueCountFrequency (%)
23.58962
 
2.3%
20.29322
 
0.8%
19.293214
7.8%
18.20367
 
2.4%
17.79939
 
1.4%
14.195187
6.8%
13.868144
5.2%
13.65653
 
1.9%
13.31641
 
1.5%
13.213208
7.6%

climate_daily_max_temp
Real number (ℝ)

High correlation  Missing 

Distinct11
Distinct (%)1.0%
Missing1616
Missing (%)58.7%
Infinite0
Infinite (%)0.0%
Mean23.182599
Minimum17.553
Maximum30.083
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.0 KiB
2025-11-25T07:34:16.747452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum17.553
5-th percentile17.553
Q121.474
median22.413
Q326.343
95-th percentile30.083
Maximum30.083
Range12.53
Interquartile range (IQR)4.869

Descriptive statistics

Standard deviation2.9483779
Coefficient of variation (CV)0.12718065
Kurtosis0.15361931
Mean23.182599
Median Absolute Deviation (MAD)1.066
Skewness0.324421
Sum26312.25
Variance8.6929324
MonotonicityNot monotonic
2025-11-25T07:34:16.784041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
26.343214
 
7.8%
22.23208
 
7.6%
23.023187
 
6.8%
21.347144
 
5.2%
17.55398
 
3.6%
22.41367
 
2.4%
30.08362
 
2.3%
21.47453
 
1.9%
20.76841
 
1.5%
25.839
 
1.4%
(Missing)1616
58.7%
ValueCountFrequency (%)
17.55398
3.6%
20.76841
 
1.5%
21.347144
5.2%
21.47453
 
1.9%
22.23208
7.6%
22.41367
 
2.4%
23.023187
6.8%
25.839
 
1.4%
26.343214
7.8%
26.76922
 
0.8%
ValueCountFrequency (%)
30.08362
 
2.3%
26.76922
 
0.8%
26.343214
7.8%
25.839
 
1.4%
23.023187
6.8%
22.41367
 
2.4%
22.23208
7.6%
21.47453
 
1.9%
21.347144
5.2%
20.76841
 
1.5%

climate_daily_min_temp
Real number (ℝ)

High correlation  Missing 

Distinct11
Distinct (%)1.0%
Missing1616
Missing (%)58.7%
Infinite0
Infinite (%)0.0%
Mean7.5503286
Minimum2.343
Maximum14.954
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.0 KiB
2025-11-25T07:34:16.820256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.343
5-th percentile2.343
Q13.763
median6.616
Q311.253
95-th percentile14.954
Maximum14.954
Range12.611
Interquartile range (IQR)7.49

Descriptive statistics

Standard deviation4.0456474
Coefficient of variation (CV)0.53582401
Kurtosis-1.0855077
Mean7.5503286
Median Absolute Deviation (MAD)2.853
Skewness0.50562955
Sum8569.623
Variance16.367263
MonotonicityNot monotonic
2025-11-25T07:34:16.857619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
11.253214
 
7.8%
3.763208
 
7.6%
4.56187
 
6.8%
7.436144
 
5.2%
2.34398
 
3.6%
14.7967
 
2.4%
14.95462
 
2.3%
6.03453
 
1.9%
6.61641
 
1.5%
10.49339
 
1.4%
(Missing)1616
58.7%
ValueCountFrequency (%)
2.34398
3.6%
3.763208
7.6%
4.56187
6.8%
6.03453
 
1.9%
6.61641
 
1.5%
7.436144
5.2%
10.49339
 
1.4%
11.253214
7.8%
13.96822
 
0.8%
14.7967
 
2.4%
ValueCountFrequency (%)
14.95462
 
2.3%
14.7967
 
2.4%
13.96822
 
0.8%
11.253214
7.8%
10.49339
 
1.4%
7.436144
5.2%
6.61641
 
1.5%
6.03453
 
1.9%
4.56187
6.8%
3.763208
7.6%

climate_7d_mean_temp
Real number (ℝ)

High correlation  Missing 

Distinct11
Distinct (%)1.0%
Missing1616
Missing (%)58.7%
Infinite0
Infinite (%)0.0%
Mean15.139061
Minimum9.215
Maximum21.742
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.0 KiB
2025-11-25T07:34:16.892375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum9.215
5-th percentile9.215
Q111.927
median16.313
Q319.038
95-th percentile21.742
Maximum21.742
Range12.527
Interquartile range (IQR)7.111

Descriptive statistics

Standard deviation3.6217705
Coefficient of variation (CV)0.2392335
Kurtosis-1.2015134
Mean15.139061
Median Absolute Deviation (MAD)3.532
Skewness0.034902052
Sum17182.834
Variance13.117222
MonotonicityNot monotonic
2025-11-25T07:34:16.929235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
19.038214
 
7.8%
16.313208
 
7.6%
11.927187
 
6.8%
12.781144
 
5.2%
9.21598
 
3.6%
18.25467
 
2.4%
21.74262
 
2.3%
10.79353
 
1.9%
12.66541
 
1.5%
16.47139
 
1.4%
(Missing)1616
58.7%
ValueCountFrequency (%)
9.21598
3.6%
10.79353
 
1.9%
11.927187
6.8%
12.66541
 
1.5%
12.781144
5.2%
16.313208
7.6%
16.47139
 
1.4%
18.25467
 
2.4%
19.038214
7.8%
19.86522
 
0.8%
ValueCountFrequency (%)
21.74262
 
2.3%
19.86522
 
0.8%
19.038214
7.8%
18.25467
 
2.4%
16.47139
 
1.4%
16.313208
7.6%
12.781144
5.2%
12.66541
 
1.5%
11.927187
6.8%
10.79353
 
1.9%

climate_7d_max_temp
Real number (ℝ)

High correlation  Missing 

Distinct11
Distinct (%)1.0%
Missing1616
Missing (%)58.7%
Infinite0
Infinite (%)0.0%
Mean25.916914
Minimum17.721
Maximum30.867
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.0 KiB
2025-11-25T07:34:16.967218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum17.721
5-th percentile17.721
Q121.977
median26.996
Q329.423
95-th percentile30.867
Maximum30.867
Range13.146
Interquartile range (IQR)7.446

Descriptive statistics

Standard deviation4.0747905
Coefficient of variation (CV)0.15722514
Kurtosis-0.91203039
Mean25.916914
Median Absolute Deviation (MAD)2.708
Skewness-0.60654343
Sum29415.697
Variance16.603917
MonotonicityNot monotonic
2025-11-25T07:34:17.004769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
29.704214
 
7.8%
29.423208
 
7.6%
25.079187
 
6.8%
21.52144
 
5.2%
17.72198
 
3.6%
26.99667
 
2.4%
30.86762
 
2.3%
21.97753
 
1.9%
20.76841
 
1.5%
26.76139
 
1.4%
(Missing)1616
58.7%
ValueCountFrequency (%)
17.72198
3.6%
20.76841
 
1.5%
21.52144
5.2%
21.97753
 
1.9%
25.079187
6.8%
26.76139
 
1.4%
26.99667
 
2.4%
28.69622
 
0.8%
29.423208
7.6%
29.704214
7.8%
ValueCountFrequency (%)
30.86762
 
2.3%
29.704214
7.8%
29.423208
7.6%
28.69622
 
0.8%
26.99667
 
2.4%
26.76139
 
1.4%
25.079187
6.8%
21.97753
 
1.9%
21.52144
5.2%
20.76841
 
1.5%

climate_14d_mean_temp
Real number (ℝ)

High correlation  Missing 

Distinct11
Distinct (%)1.0%
Missing1616
Missing (%)58.7%
Infinite0
Infinite (%)0.0%
Mean16.042067
Minimum10.426
Maximum21.69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.0 KiB
2025-11-25T07:34:17.040767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10.426
5-th percentile10.426
Q112.258
median18.254
Q319.069
95-th percentile21.69
Maximum21.69
Range11.264
Interquartile range (IQR)6.811

Descriptive statistics

Standard deviation3.3876747
Coefficient of variation (CV)0.21117446
Kurtosis-1.3067725
Mean16.042067
Median Absolute Deviation (MAD)3.436
Skewness-0.22262646
Sum18207.746
Variance11.47634
MonotonicityNot monotonic
2025-11-25T07:34:17.078146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
19.069214
 
7.8%
18.483208
 
7.6%
14.595187
 
6.8%
12.258144
 
5.2%
10.42698
 
3.6%
18.25467
 
2.4%
21.6962
 
2.3%
11.53253
 
1.9%
12.5741
 
1.5%
16.05739
 
1.4%
(Missing)1616
58.7%
ValueCountFrequency (%)
10.42698
3.6%
11.53253
 
1.9%
12.258144
5.2%
12.5741
 
1.5%
14.595187
6.8%
16.05739
 
1.4%
18.25467
 
2.4%
18.483208
7.6%
19.069214
7.8%
20.26222
 
0.8%
ValueCountFrequency (%)
21.6962
 
2.3%
20.26222
 
0.8%
19.069214
7.8%
18.483208
7.6%
18.25467
 
2.4%
16.05739
 
1.4%
14.595187
6.8%
12.5741
 
1.5%
12.258144
5.2%
11.53253
 
1.9%

climate_30d_mean_temp
Real number (ℝ)

High correlation  Missing 

Distinct11
Distinct (%)1.0%
Missing1616
Missing (%)58.7%
Infinite0
Infinite (%)0.0%
Mean16.024641
Minimum10.635
Maximum21.041
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.0 KiB
2025-11-25T07:34:17.112729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10.635
5-th percentile10.635
Q111.635
median18.576
Q318.854
95-th percentile21.041
Maximum21.041
Range10.406
Interquartile range (IQR)7.219

Descriptive statistics

Standard deviation3.4391822
Coefficient of variation (CV)0.21461837
Kurtosis-1.3241931
Mean16.024641
Median Absolute Deviation (MAD)2.465
Skewness-0.42123309
Sum18187.967
Variance11.827974
MonotonicityNot monotonic
2025-11-25T07:34:17.147668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
18.854214
 
7.8%
18.576208
 
7.6%
15.421187
 
6.8%
11.076144
 
5.2%
10.63598
 
3.6%
18.79467
 
2.4%
21.04162
 
2.3%
11.63553
 
1.9%
12.85641
 
1.5%
15.77539
 
1.4%
(Missing)1616
58.7%
ValueCountFrequency (%)
10.63598
3.6%
11.076144
5.2%
11.63553
 
1.9%
12.85641
 
1.5%
15.421187
6.8%
15.77539
 
1.4%
18.576208
7.6%
18.79467
 
2.4%
18.854214
7.8%
20.26322
 
0.8%
ValueCountFrequency (%)
21.04162
 
2.3%
20.26322
 
0.8%
18.854214
7.8%
18.79467
 
2.4%
18.576208
7.6%
15.77539
 
1.4%
15.421187
6.8%
12.85641
 
1.5%
11.63553
 
1.9%
11.076144
5.2%

climate_temp_anomaly
Real number (ℝ)

High correlation  Missing 

Distinct11
Distinct (%)1.0%
Missing1616
Missing (%)58.7%
Infinite0
Infinite (%)0.0%
Mean7.1579163
Minimum3.618
Maximum10.271
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.0 KiB
2025-11-25T07:34:17.180369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3.618
5-th percentile3.618
Q16.505
median7.489
Q39.042
95-th percentile10.271
Maximum10.271
Range6.653
Interquartile range (IQR)2.537

Descriptive statistics

Standard deviation2.2633511
Coefficient of variation (CV)0.31620252
Kurtosis-0.9276673
Mean7.1579163
Median Absolute Deviation (MAD)1.553
Skewness-0.39512055
Sum8124.235
Variance5.1227584
MonotonicityNot monotonic
2025-11-25T07:34:17.214716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
7.489214
 
7.8%
3.654208
 
7.6%
7.602187
 
6.8%
10.271144
 
5.2%
6.91898
 
3.6%
3.61867
 
2.4%
9.04262
 
2.3%
9.83953
 
1.9%
7.91341
 
1.5%
10.02539
 
1.4%
(Missing)1616
58.7%
ValueCountFrequency (%)
3.61867
 
2.4%
3.654208
7.6%
6.50522
 
0.8%
6.91898
3.6%
7.489214
7.8%
7.602187
6.8%
7.91341
 
1.5%
9.04262
 
2.3%
9.83953
 
1.9%
10.02539
 
1.4%
ValueCountFrequency (%)
10.271144
5.2%
10.02539
 
1.4%
9.83953
 
1.9%
9.04262
 
2.3%
7.91341
 
1.5%
7.602187
6.8%
7.489214
7.8%
6.91898
3.6%
6.50522
 
0.8%
3.654208
7.6%

climate_standardized_anomaly
Real number (ℝ)

High correlation  Missing 

Distinct11
Distinct (%)1.0%
Missing1616
Missing (%)58.7%
Infinite0
Infinite (%)0.0%
Mean-0.19795242
Minimum-1.853
Maximum1.905
Zeros0
Zeros (%)0.0%
Negative560
Negative (%)20.4%
Memory size43.0 KiB
2025-11-25T07:34:17.249981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1.853
5-th percentile-1.853
Q1-1.189
median0.007
Q31.074
95-th percentile1.905
Maximum1.905
Range3.758
Interquartile range (IQR)2.263

Descriptive statistics

Standard deviation1.3126605
Coefficient of variation (CV)-6.6311919
Kurtosis-1.2451726
Mean-0.19795242
Median Absolute Deviation (MAD)1.099
Skewness0.36631188
Sum-224.676
Variance1.7230776
MonotonicityNot monotonic
2025-11-25T07:34:17.290260image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0.007214
 
7.8%
-1.853208
 
7.6%
-1.092187
 
6.8%
1.781144
 
5.2%
-1.18998
 
3.6%
-0.75267
 
2.4%
1.90562
 
2.3%
1.60453
 
1.9%
0.1941
 
1.5%
1.07439
 
1.4%
(Missing)1616
58.7%
ValueCountFrequency (%)
-1.853208
7.6%
-1.18998
3.6%
-1.092187
6.8%
-0.75267
 
2.4%
0.007214
7.8%
0.1941
 
1.5%
0.95922
 
0.8%
1.07439
 
1.4%
1.60453
 
1.9%
1.781144
5.2%
ValueCountFrequency (%)
1.90562
 
2.3%
1.781144
5.2%
1.60453
 
1.9%
1.07439
 
1.4%
0.95922
 
0.8%
0.1941
 
1.5%
0.007214
7.8%
-0.75267
 
2.4%
-1.092187
6.8%
-1.18998
3.6%

climate_heat_day_p90
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.2%
Missing1616
Missing (%)58.7%
Memory size167.5 KiB
0.0
1073 
1.0
 
62

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3405
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01073
39.0%
1.062
 
2.3%
(Missing)1616
58.7%

Length

2025-11-25T07:34:17.336139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:34:17.371532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01073
94.5%
1.062
 
5.5%

Most occurring characters

ValueCountFrequency (%)
02208
64.8%
.1135
33.3%
162
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2270
66.7%
Other Punctuation1135
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02208
97.3%
162
 
2.7%
Other Punctuation
ValueCountFrequency (%)
.1135
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3405
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02208
64.8%
.1135
33.3%
162
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII3405
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02208
64.8%
.1135
33.3%
162
 
1.8%

climate_heat_day_p95
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.2%
Missing1616
Missing (%)58.7%
Memory size167.5 KiB
0.0
1073 
1.0
 
62

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3405
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01073
39.0%
1.062
 
2.3%
(Missing)1616
58.7%

Length

2025-11-25T07:34:17.406085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:34:17.438661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01073
94.5%
1.062
 
5.5%

Most occurring characters

ValueCountFrequency (%)
02208
64.8%
.1135
33.3%
162
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2270
66.7%
Other Punctuation1135
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02208
97.3%
162
 
2.7%
Other Punctuation
ValueCountFrequency (%)
.1135
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3405
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02208
64.8%
.1135
33.3%
162
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII3405
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02208
64.8%
.1135
33.3%
162
 
1.8%

climate_heat_stress_index
Real number (ℝ)

High correlation  Missing 

Distinct11
Distinct (%)1.0%
Missing1616
Missing (%)58.7%
Infinite0
Infinite (%)0.0%
Mean18.312848
Minimum13.428
Maximum27.393
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.0 KiB
2025-11-25T07:34:17.467251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum13.428
5-th percentile13.639
Q114.306
median17.923
Q321.523
95-th percentile27.393
Maximum27.393
Range13.965
Interquartile range (IQR)7.217

Descriptive statistics

Standard deviation3.536553
Coefficient of variation (CV)0.19311867
Kurtosis0.20907555
Mean18.312848
Median Absolute Deviation (MAD)3.6
Skewness0.58250383
Sum20785.083
Variance12.507207
MonotonicityNot monotonic
2025-11-25T07:34:17.503426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
21.523214
 
7.8%
19.275208
 
7.6%
17.347187
 
6.8%
14.306144
 
5.2%
13.63998
 
3.6%
17.92367
 
2.4%
27.39362
 
2.3%
13.42853
 
1.9%
15.72141
 
1.5%
19.95839
 
1.4%
(Missing)1616
58.7%
ValueCountFrequency (%)
13.42853
 
1.9%
13.63998
3.6%
14.306144
5.2%
15.72141
 
1.5%
17.347187
6.8%
17.92367
 
2.4%
19.275208
7.6%
19.95839
 
1.4%
21.523214
7.8%
22.52622
 
0.8%
ValueCountFrequency (%)
27.39362
 
2.3%
22.52622
 
0.8%
21.523214
7.8%
19.95839
 
1.4%
19.275208
7.6%
17.92367
 
2.4%
17.347187
6.8%
15.72141
 
1.5%
14.306144
5.2%
13.63998
3.6%

climate_p90_threshold
Categorical

Constant  Missing 

Distinct1
Distinct (%)0.1%
Missing1616
Missing (%)58.7%
Memory size170.8 KiB
28.409
1135 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters6810
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row28.409
2nd row28.409
3rd row28.409
4th row28.409
5th row28.409

Common Values

ValueCountFrequency (%)
28.4091135
41.3%
(Missing)1616
58.7%

Length

2025-11-25T07:34:17.545621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:34:17.577086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
28.4091135
100.0%

Most occurring characters

ValueCountFrequency (%)
21135
16.7%
81135
16.7%
.1135
16.7%
41135
16.7%
01135
16.7%
91135
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5675
83.3%
Other Punctuation1135
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
21135
20.0%
81135
20.0%
41135
20.0%
01135
20.0%
91135
20.0%
Other Punctuation
ValueCountFrequency (%)
.1135
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common6810
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
21135
16.7%
81135
16.7%
.1135
16.7%
41135
16.7%
01135
16.7%
91135
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII6810
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21135
16.7%
81135
16.7%
.1135
16.7%
41135
16.7%
01135
16.7%
91135
16.7%

climate_p95_threshold
Categorical

Constant  Missing 

Distinct1
Distinct (%)0.1%
Missing1616
Missing (%)58.7%
Memory size170.8 KiB
29.704
1135 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters6810
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row29.704
2nd row29.704
3rd row29.704
4th row29.704
5th row29.704

Common Values

ValueCountFrequency (%)
29.7041135
41.3%
(Missing)1616
58.7%

Length

2025-11-25T07:34:17.610726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:34:17.643060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
29.7041135
100.0%

Most occurring characters

ValueCountFrequency (%)
21135
16.7%
91135
16.7%
.1135
16.7%
71135
16.7%
01135
16.7%
41135
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5675
83.3%
Other Punctuation1135
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
21135
20.0%
91135
20.0%
71135
20.0%
01135
20.0%
41135
20.0%
Other Punctuation
ValueCountFrequency (%)
.1135
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common6810
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
21135
16.7%
91135
16.7%
.1135
16.7%
71135
16.7%
01135
16.7%
41135
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII6810
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21135
16.7%
91135
16.7%
.1135
16.7%
71135
16.7%
01135
16.7%
41135
16.7%

climate_p99_threshold
Categorical

Constant  Missing 

Distinct1
Distinct (%)0.1%
Missing1616
Missing (%)58.7%
Memory size170.8 KiB
31.797
1135 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters6810
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row31.797
2nd row31.797
3rd row31.797
4th row31.797
5th row31.797

Common Values

ValueCountFrequency (%)
31.7971135
41.3%
(Missing)1616
58.7%

Length

2025-11-25T07:34:17.677109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:34:17.709379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
31.7971135
100.0%

Most occurring characters

ValueCountFrequency (%)
72270
33.3%
31135
16.7%
11135
16.7%
.1135
16.7%
91135
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5675
83.3%
Other Punctuation1135
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
72270
40.0%
31135
20.0%
11135
20.0%
91135
20.0%
Other Punctuation
ValueCountFrequency (%)
.1135
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common6810
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
72270
33.3%
31135
16.7%
11135
16.7%
.1135
16.7%
91135
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII6810
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
72270
33.3%
31135
16.7%
11135
16.7%
.1135
16.7%
91135
16.7%

climate_season
Categorical

High correlation  Missing 

Distinct4
Distinct (%)0.4%
Missing1616
Missing (%)58.7%
Memory size170.8 KiB
Spring
609 
Winter
295 
Summer
129 
Autumn
102 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters6810
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAutumn
2nd rowSpring
3rd rowWinter
4th rowSpring
5th rowSpring

Common Values

ValueCountFrequency (%)
Spring609
 
22.1%
Winter295
 
10.7%
Summer129
 
4.7%
Autumn102
 
3.7%
(Missing)1616
58.7%

Length

2025-11-25T07:34:17.742918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:34:17.779535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
spring609
53.7%
winter295
26.0%
summer129
 
11.4%
autumn102
 
9.0%

Most occurring characters

ValueCountFrequency (%)
r1033
15.2%
n1006
14.8%
i904
13.3%
S738
10.8%
p609
8.9%
g609
8.9%
e424
6.2%
t397
 
5.8%
m360
 
5.3%
u333
 
4.9%
Other values (2)397
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5675
83.3%
Uppercase Letter1135
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r1033
18.2%
n1006
17.7%
i904
15.9%
p609
10.7%
g609
10.7%
e424
7.5%
t397
 
7.0%
m360
 
6.3%
u333
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
S738
65.0%
W295
 
26.0%
A102
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6810
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r1033
15.2%
n1006
14.8%
i904
13.3%
S738
10.8%
p609
8.9%
g609
8.9%
e424
6.2%
t397
 
5.8%
m360
 
5.3%
u333
 
4.9%
Other values (2)397
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII6810
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r1033
15.2%
n1006
14.8%
i904
13.3%
S738
10.8%
p609
8.9%
g609
8.9%
e424
6.2%
t397
 
5.8%
m360
 
5.3%
u333
 
4.9%
Other values (2)397
 
5.8%

Interactions

2025-11-25T07:34:14.049365image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:07.454191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:07.970870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:08.532641image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:09.015577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:09.448256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:09.945155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:10.547478image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:11.025352image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:11.508882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:11.996576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:12.480160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:13.037067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:13.521855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:14.085003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:07.489287image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:08.004004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:08.564004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:09.044658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:09.482141image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:10.013115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:10.579213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:11.059086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:11.542318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:12.030375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:12.514058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:13.067993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:13.557166image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:14.121504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:07.520607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:08.036724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:08.596589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:09.072621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:09.517069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:10.048121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:10.612537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:11.091046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:11.575750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:12.062943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:12.547117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:13.101652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:13.594804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:14.157099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:07.552474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:08.069704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:08.629387image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:09.104746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:09.551684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:10.083626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:10.646431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:11.126840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:11.611840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:12.097274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:12.581060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:13.135896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:13.632791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:14.189871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:07.599696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:08.102287image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:08.660937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:09.136398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:09.583063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:10.113729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:10.676771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:11.156757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:11.642120image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:12.129035image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:12.608915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:13.166506image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:13.665849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:14.225823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:07.652756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:08.136329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:08.698118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:09.168665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:09.618431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:10.149916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:10.712087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:11.192908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:11.676805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:12.163333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:12.643235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:13.204492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:13.704244image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:14.261327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:07.686139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:08.258544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:08.731056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:09.200258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:09.656100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:10.182719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:10.749844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:11.228280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:11.710859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:12.197359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:12.759951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:13.240925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:13.743990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:14.300194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:07.728485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:08.292322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:08.767274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:09.230441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:09.691480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:10.217458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:10.783479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:11.265374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:11.747695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:12.231799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:12.793234image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:13.278262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:13.780555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:14.335638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:07.763411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:08.325887image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:08.801892image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:09.261189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:09.728562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:10.254452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:10.817902image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:11.299850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:11.783077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:12.267815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:12.826595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:13.312308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:13.819170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:14.371505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:07.797735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:08.359178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:08.838271image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:09.292640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:09.763345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:10.290002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:10.851849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:11.335486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:11.818997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:12.302837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:12.858928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:13.346446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:13.858783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:14.405711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:07.832599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:08.392784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:08.873581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:09.323970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:09.798230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:10.325008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:10.885569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:11.369394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:11.853767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:12.337911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:12.894149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:13.381537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:13.897939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:14.439315image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:07.864603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:08.424268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:08.906773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:09.352325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:09.829817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:10.357126image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:10.917813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:11.400769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:11.887546image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:12.370613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:12.924254image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:13.414381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:13.933210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:14.474454image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:07.897824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:08.458893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:08.941076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:09.382420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:09.865586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:10.473666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:10.952699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:11.436299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:11.922652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:12.404858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:12.963766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:13.447582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:13.970612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:14.514059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:07.935376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:08.496903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:08.978008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:09.416437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:09.904175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:10.510607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:10.989591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:11.473055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:11.960284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:12.444101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:13.001368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:13.485400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:34:14.009121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-11-25T07:34:17.814975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Age (at enrolment)CD4 cell count (cells/µL)HIV viral load (copies/mL)Sexclimate_14d_mean_tempclimate_30d_mean_tempclimate_7d_max_tempclimate_7d_mean_tempclimate_daily_max_tempclimate_daily_mean_tempclimate_daily_min_tempclimate_heat_day_p90climate_heat_day_p95climate_heat_stress_indexclimate_seasonclimate_standardized_anomalyclimate_temp_anomalymonthyear
Age (at enrolment)1.000-0.130-0.0880.2000.0270.0280.0230.0330.0120.0210.0280.0520.0520.0250.0410.005-0.0200.0190.044
CD4 cell count (cells/µL)-0.1301.0000.0310.1680.0070.004-0.003-0.0040.0380.0420.0180.0000.0000.0080.0000.0240.033-0.0160.042
HIV viral load (copies/mL)-0.0880.0311.0000.0990.0210.0370.0040.0420.0820.0970.0740.0000.0000.0350.0560.041-0.010-0.0530.024
Sex0.2000.1680.0991.0000.0000.0000.0510.0600.0000.0000.0420.0000.0000.0270.0000.0360.0000.0720.058
climate_14d_mean_temp0.0270.0070.0210.0001.0000.9780.9680.9120.8390.6500.5270.9970.9970.9830.9250.006-0.3450.4330.997
climate_30d_mean_temp0.0280.0040.0370.0000.9781.0000.9560.9120.8610.7140.6150.8490.8490.9540.7740.049-0.3700.4680.849
climate_7d_max_temp0.023-0.0030.0040.0510.9680.9561.0000.8710.8250.6140.4860.4170.4170.9410.793-0.022-0.3360.4960.417
climate_7d_mean_temp0.033-0.0040.0420.0600.9120.9120.8711.0000.7360.7070.7360.9980.9980.9160.6640.278-0.2060.3510.998
climate_daily_max_temp0.0120.0380.0820.0000.8390.8610.8250.7361.0000.8830.6470.9980.9980.8590.7600.203-0.0370.2880.998
climate_daily_mean_temp0.0210.0420.0970.0000.6500.7140.6140.7070.8831.0000.9000.9980.9980.6720.7380.5640.2200.1630.998
climate_daily_min_temp0.0280.0180.0740.0420.5270.6150.4860.7360.6470.9001.0000.6090.6090.5370.9410.7440.2660.0760.609
climate_heat_day_p900.0520.0000.0000.0000.9970.8490.4170.9980.9980.9980.6091.0000.9910.9970.6700.4360.9980.9960.991
climate_heat_day_p950.0520.0000.0000.0000.9970.8490.4170.9980.9980.9980.6090.9911.0000.9970.6700.4360.9980.9960.991
climate_heat_stress_index0.0250.0080.0350.0270.9830.9540.9410.9160.8590.6720.5370.9970.9971.0000.9330.037-0.2950.3770.997
climate_season0.0410.0000.0560.0000.9250.7740.7930.6640.7600.7380.9410.6700.6700.9331.0000.8170.7850.9140.670
climate_standardized_anomaly0.0050.0240.0410.0360.0060.049-0.0220.2780.2030.5640.7440.4360.4360.0370.8171.0000.782-0.4970.436
climate_temp_anomaly-0.0200.033-0.0100.000-0.345-0.370-0.336-0.206-0.0370.2200.2660.9980.998-0.2950.7850.7821.000-0.6890.998
month0.019-0.016-0.0530.0720.4330.4680.4960.3510.2880.1630.0760.9960.9960.3770.914-0.497-0.6891.0000.387
year0.0440.0420.0240.0580.9970.8490.4170.9980.9980.9980.6090.9910.9910.9970.6700.4360.9980.3871.000

Missing values

2025-11-25T07:34:14.582319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-25T07:34:14.833541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-25T07:34:14.965450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

study_sourceprimary_dateyearmonthlatitudelongitudejhb_subregioncityprovincecountryAge (at enrolment)SexdateLocation of study follow-upstudy_site_locationCD4 cell count (cells/µL)HIV viral load (copies/mL)Clinical Study IDHIV_statustotal_protein_extreme_flagclimate_daily_mean_tempclimate_daily_max_tempclimate_daily_min_tempclimate_7d_mean_tempclimate_7d_max_tempclimate_14d_mean_tempclimate_30d_mean_tempclimate_temp_anomalyclimate_standardized_anomalyclimate_heat_day_p90climate_heat_day_p95climate_heat_stress_indexclimate_p90_thresholdclimate_p95_thresholdclimate_p99_thresholdclimate_season
3377JHB_Aurum_0092014-02-152014.02.0-25.747928.2293Eastern_JHBJohannesburgGautengSouth Africa24.0Female2014-02-15Aurum Institute - Multi-site Gauteng and LimpopoTembisa/East Rand (Aurum Institute)369.00.0Tholimpilo_HIV_Linkage_StudyPositive0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3378JHB_Aurum_0092014-04-092014.04.0-25.747928.2293Eastern_JHBJohannesburgGautengSouth Africa38.0Female2014-04-09Aurum Institute - Multi-site Gauteng and LimpopoTembisa/East Rand (Aurum Institute)701.0NaNTholimpilo_HIV_Linkage_StudyPositive0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3379JHB_Aurum_0092014-08-122014.08.0-25.747928.2293Eastern_JHBJohannesburgGautengSouth Africa21.0Male2014-08-12Aurum Institute - Multi-site Gauteng and LimpopoTembisa/East Rand (Aurum Institute)654.0NaNTholimpilo_HIV_Linkage_StudyPositive0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3380JHB_Aurum_0092014-04-292014.04.0-25.747928.2293Eastern_JHBJohannesburgGautengSouth Africa29.0Male2014-04-29Aurum Institute - Multi-site Gauteng and LimpopoTembisa/East Rand (Aurum Institute)350.0NaNTholimpilo_HIV_Linkage_StudyPositive0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3381JHB_Aurum_0092013-04-292013.04.0-25.747928.2293Eastern_JHBJohannesburgGautengSouth Africa35.0Female2013-04-29Aurum Institute - Multi-site Gauteng and LimpopoTembisa/East Rand (Aurum Institute)324.00.0Tholimpilo_HIV_Linkage_StudyPositive0.017.79925.80010.49316.47126.76116.05715.77510.0251.0740.00.019.95828.40929.70431.797Autumn
3382JHB_Aurum_0092014-06-262014.06.0-25.747928.2293Eastern_JHBJohannesburgGautengSouth Africa22.0Male2014-06-26Aurum Institute - Multi-site Gauteng and LimpopoTembisa/East Rand (Aurum Institute)276.0NaNTholimpilo_HIV_Linkage_StudyPositive0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3383JHB_Aurum_0092013-11-192013.011.0-25.747928.2293Eastern_JHBJohannesburgGautengSouth Africa38.0Female2013-11-19Aurum Institute - Multi-site Gauteng and LimpopoTembisa/East Rand (Aurum Institute)NaNNaNTholimpilo_HIV_Linkage_StudyPositive0.019.29326.34311.25319.03829.70419.06918.8547.4890.0070.00.021.52328.40929.70431.797Spring
3384JHB_Aurum_0092014-09-082014.09.0-25.747928.2293Eastern_JHBJohannesburgGautengSouth AfricaNaNMale2014-09-08Aurum Institute - Multi-site Gauteng and LimpopoTembisa/East Rand (Aurum Institute)NaNNaNTholimpilo_HIV_Linkage_StudyPositive0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3385JHB_Aurum_0092013-08-242013.08.0-25.747928.2293Eastern_JHBJohannesburgGautengSouth Africa22.0Female2013-08-24Aurum Institute - Multi-site Gauteng and LimpopoTembisa/East Rand (Aurum Institute)525.0NaNTholimpilo_HIV_Linkage_StudyPositive0.09.35617.5532.3439.21517.72110.42610.6356.918-1.1890.00.013.63928.40929.70431.797Winter
3386JHB_Aurum_0092014-03-242014.03.0-25.747928.2293Eastern_JHBJohannesburgGautengSouth Africa42.0Male2014-03-24Aurum Institute - Multi-site Gauteng and LimpopoTembisa/East Rand (Aurum Institute)287.0NaNTholimpilo_HIV_Linkage_StudyPositive0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
study_sourceprimary_dateyearmonthlatitudelongitudejhb_subregioncityprovincecountryAge (at enrolment)SexdateLocation of study follow-upstudy_site_locationCD4 cell count (cells/µL)HIV viral load (copies/mL)Clinical Study IDHIV_statustotal_protein_extreme_flagclimate_daily_mean_tempclimate_daily_max_tempclimate_daily_min_tempclimate_7d_mean_tempclimate_7d_max_tempclimate_14d_mean_tempclimate_30d_mean_tempclimate_temp_anomalyclimate_standardized_anomalyclimate_heat_day_p90climate_heat_day_p95climate_heat_stress_indexclimate_p90_thresholdclimate_p95_thresholdclimate_p99_thresholdclimate_season
6118JHB_Aurum_0092013-07-172013.07.0-25.747928.2293Eastern_JHBJohannesburgGautengSouth Africa23.0Male2013-07-17Aurum Institute - Multi-site Gauteng and LimpopoTembisa/East Rand (Aurum Institute)174.0NaNTholimpilo_HIV_Linkage_StudyPositive0.013.86821.3477.43612.78121.52012.25811.07610.2711.7810.00.014.30628.40929.70431.797Winter
6119JHB_Aurum_0092013-06-062013.06.0-25.747928.2293Eastern_JHBJohannesburgGautengSouth Africa36.0Male2013-06-06Aurum Institute - Multi-site Gauteng and LimpopoTembisa/East Rand (Aurum Institute)110.0NaNTholimpilo_HIV_Linkage_StudyPositive0.013.65621.4746.03410.79321.97711.53211.6359.8391.6040.00.013.42828.40929.70431.797Winter
6120JHB_Aurum_0092014-06-172014.06.0-25.747928.2293Eastern_JHBJohannesburgGautengSouth Africa29.0Male2014-06-17Aurum Institute - Multi-site Gauteng and LimpopoTembisa/East Rand (Aurum Institute)393.00.0Tholimpilo_HIV_Linkage_StudyPositive0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
6121JHB_Aurum_0092014-02-032014.02.0-25.747928.2293Eastern_JHBJohannesburgGautengSouth Africa34.0Female2014-02-03Aurum Institute - Multi-site Gauteng and LimpopoTembisa/East Rand (Aurum Institute)202.0NaNTholimpilo_HIV_Linkage_StudyPositive0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
6122JHB_Aurum_0092014-04-292014.04.0-25.747928.2293Eastern_JHBJohannesburgGautengSouth Africa34.0Female2014-04-29Aurum Institute - Multi-site Gauteng and LimpopoTembisa/East Rand (Aurum Institute)31.0NaNTholimpilo_HIV_Linkage_StudyPositive0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
6123JHB_Aurum_0092014-04-232014.04.0-25.747928.2293Eastern_JHBJohannesburgGautengSouth Africa31.0Male2014-04-23Aurum Institute - Multi-site Gauteng and LimpopoTembisa/East Rand (Aurum Institute)365.0NaNTholimpilo_HIV_Linkage_StudyPositive0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
6124JHB_Aurum_0092013-08-272013.08.0-25.747928.2293Eastern_JHBJohannesburgGautengSouth Africa31.0Female2013-08-27Aurum Institute - Multi-site Gauteng and LimpopoTembisa/East Rand (Aurum Institute)586.0NaNTholimpilo_HIV_Linkage_StudyPositive0.09.35617.5532.3439.21517.72110.42610.6356.918-1.1890.00.013.63928.40929.70431.797Winter
6125JHB_Aurum_0092014-08-142014.08.0-25.747928.2293Eastern_JHBJohannesburgGautengSouth Africa65.0Male2014-08-14Aurum Institute - Multi-site Gauteng and LimpopoTembisa/East Rand (Aurum Institute)409.0NaNTholimpilo_HIV_Linkage_StudyPositive0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
6126JHB_Aurum_0092014-08-042014.08.0-25.747928.2293Eastern_JHBJohannesburgGautengSouth Africa28.0Male2014-08-04Aurum Institute - Multi-site Gauteng and LimpopoTembisa/East Rand (Aurum Institute)455.0NaNTholimpilo_HIV_Linkage_StudyPositive0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
6127JHB_Aurum_0092013-11-162013.011.0-25.747928.2293Eastern_JHBJohannesburgGautengSouth Africa23.0Male2013-11-16Aurum Institute - Multi-site Gauteng and LimpopoTembisa/East Rand (Aurum Institute)300.0NaNTholimpilo_HIV_Linkage_StudyPositive0.019.29326.34311.25319.03829.70419.06918.8547.4890.0070.00.021.52328.40929.70431.797Spring

Duplicate rows

Most frequently occurring

study_sourceprimary_dateyearmonthlatitudelongitudejhb_subregioncityprovincecountryAge (at enrolment)SexdateLocation of study follow-upstudy_site_locationCD4 cell count (cells/µL)HIV viral load (copies/mL)Clinical Study IDHIV_statustotal_protein_extreme_flagclimate_daily_mean_tempclimate_daily_max_tempclimate_daily_min_tempclimate_7d_mean_tempclimate_7d_max_tempclimate_14d_mean_tempclimate_30d_mean_tempclimate_temp_anomalyclimate_standardized_anomalyclimate_heat_day_p90climate_heat_day_p95climate_heat_stress_indexclimate_p90_thresholdclimate_p95_thresholdclimate_p99_thresholdclimate_season# duplicates
0JHB_Aurum_0092013-07-152013.07.0-25.747928.2293Eastern_JHBJohannesburgGautengSouth Africa23.0Male2013-07-15Aurum Institute - Multi-site Gauteng and LimpopoTembisa/East Rand (Aurum Institute)NaNNaNTholimpilo_HIV_Linkage_StudyPositive0.013.86821.3477.43612.78121.5212.25811.07610.2711.7810.00.014.30628.40929.70431.797Winter2
1JHB_Aurum_0092014-03-292014.03.0-25.747928.2293Eastern_JHBJohannesburgGautengSouth Africa32.0Female2014-03-29Aurum Institute - Multi-site Gauteng and LimpopoTembisa/East Rand (Aurum Institute)NaNNaNTholimpilo_HIV_Linkage_StudyPositive0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2
2JHB_Aurum_0092014-04-022014.04.0-25.747928.2293Eastern_JHBJohannesburgGautengSouth Africa49.0Male2014-04-02Aurum Institute - Multi-site Gauteng and LimpopoTembisa/East Rand (Aurum Institute)NaNNaNTholimpilo_HIV_Linkage_StudyPositive0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2
3JHB_Aurum_0092014-04-032014.04.0-25.747928.2293Eastern_JHBJohannesburgGautengSouth Africa27.0Male2014-04-03Aurum Institute - Multi-site Gauteng and LimpopoTembisa/East Rand (Aurum Institute)NaNNaNTholimpilo_HIV_Linkage_StudyPositive0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2
4JHB_Aurum_0092014-07-022014.07.0-25.747928.2293Eastern_JHBJohannesburgGautengSouth Africa39.0Male2014-07-02Aurum Institute - Multi-site Gauteng and LimpopoTembisa/East Rand (Aurum Institute)NaNNaNTholimpilo_HIV_Linkage_StudyPositive0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2
5JHB_Aurum_0092014-08-122014.08.0-25.747928.2293Eastern_JHBJohannesburgGautengSouth Africa39.0Male2014-08-12Aurum Institute - Multi-site Gauteng and LimpopoTembisa/East Rand (Aurum Institute)NaNNaNTholimpilo_HIV_Linkage_StudyPositive0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2
6JHB_Aurum_0092014-10-282014.010.0-25.747928.2293Eastern_JHBJohannesburgGautengSouth Africa37.0Female2014-10-28Aurum Institute - Multi-site Gauteng and LimpopoTembisa/East Rand (Aurum Institute)NaNNaNTholimpilo_HIV_Linkage_StudyPositive0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2